void CNN::feedForward(mat& fout, const mat& fin) {
// First 1st layer of CNN MUST have only 1 input feature map
vector<mat> fins(1);
// Transpose the input feature (fin) so that rows = feature dimension, cols =
// the number of data in a single batch.
// FIXME the last column in fin is the bias needed ONLY by DNN, not by CNN.
fins[0].resize(fin.getRows(), fin.getCols() - 1);
memcpy2D(fins[0], fin, 0, 0, fin.getRows(), fin.getCols() - 1, 0, 0);
fins[0] = ~fins[0];
// FIXME SubSamplingLayer does NOT need temporary buffer.
// MAYBE just reserve those for ConvolutionalLayer.
_houts.resize(_transforms.size());
_transforms[0]->feedForward(_houts[0], fins);
for (size_t i=1; i<_transforms.size(); ++i)
_transforms[i]->feedForward(_houts[i], _houts[i-1]);
// Concatenate
fout = ~concat(_houts.back());
// Reserve one more column for bias
fout.reserve(fout.size() + fout.getRows());
fout.resize(fout.getRows(), fout.getCols() + 1);
fillLastColumnWith(fout, 1.0f);
}
